2026-07-072026-07-07http://salesiana.dossiersoluciones.com/handle/123456789/133073Neural networks are used extensively in classification problems in particle physics research. Since the training of neural networks can be viewed as a problem of inference, Bayesian learning of neural networks can provide more optimal and robust results than conventional learning methods. We have investigated the use of Bayesian neural networks for signal/background discrimination in the search for second generation leptoquarks at the Tevatron, as an example. We present a comparison of the results obtained from the conventional training of feedforward neural networks and networks trained with Bayesian methods.3 pages, 4 figures, conference proceedingsData Analysis, Statistics and Probabilitytext